wehub-resource-sync
449 lines
18 KiB
Python
449 lines
18 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2024 The Qwen team, Alibaba Group and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes for Qwen2."""
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from __future__ import annotations
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import json
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import os
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import unicodedata
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from functools import lru_cache
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from typing import Any, Dict, List, Optional, Tuple
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import regex as re
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from ...utils.log import logger
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from .. import AddedToken, PretrainedTokenizer
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.json",
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"merges_file": "merges.txt",
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}
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__all__ = ["Qwen2Tokenizer"]
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MAX_MODEL_INPUT_SIZES = {"__internal_testing__/tiny-random-qwen2": 32768}
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PRETOKENIZE_REGEX = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
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@lru_cache()
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def bytes_to_unicode():
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"""
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Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
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characters the bpe code barfs on.
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The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
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if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
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decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
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tables between utf-8 bytes and unicode strings.
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"""
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bs = (
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list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
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)
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cs = bs[:]
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n = 0
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for b in range(2**8):
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if b not in bs:
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bs.append(b)
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cs.append(2**8 + n)
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n += 1
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cs = [chr(n) for n in cs]
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return dict(zip(bs, cs))
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def get_pairs(word):
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"""
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Return set of symbol pairs in a word.
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Word is represented as tuple of symbols (symbols being variable-length strings).
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"""
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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return pairs
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class Qwen2Tokenizer(PretrainedTokenizer):
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"""
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Construct a Qwen2 tokenizer. Based on byte-level Byte-Pair-Encoding.
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Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
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be encoded differently whether it is at the beginning of the sentence (without space) or not:
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```python
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>>> from transformers import Qwen2Tokenizer
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>>> tokenizer = Qwen2Tokenizer.from_pretrained("Qwen/Qwen-tokenizer")
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>>> tokenizer("Hello world")["input_ids"]
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[9707, 1879]
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>>> tokenizer(" Hello world")["input_ids"]
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[21927, 1879]
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```
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This is expected.
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You should not use GPT2Tokenizer instead, because of the different pretokenization rules.
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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merges_file (`str`):
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Path to the merges file.
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errors (`str`, *optional*, defaults to `"replace"`):
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Paradigm to follow when decoding bytes to UTF-8. See
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[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
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unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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bos_token (`str`, *optional*):
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The beginning of sequence token. Not applicable for this tokenizer.
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eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
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The end of sequence token.
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pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
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The token used for padding, for example when batching sequences of different lengths.
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clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
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Whether or not the model should cleanup the spaces that were added when splitting the input text during the
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tokenization process. Not applicable to this tokenizer, since tokenization does not add spaces.
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split_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the special tokens should be split during the tokenization process. The default behavior is
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to not split special tokens. This means that if `<|endoftext|>` is the `eos_token`, then `tokenizer.tokenize("<|endoftext|>") =
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['<|endoftext|>`]. Otherwise, if `split_special_tokens=True`, then `tokenizer.tokenize("<|endoftext|>")` will be give `['<',
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'|', 'endo', 'ft', 'ext', '|', '>']`. This argument is only supported for `slow` tokenizers for the moment.
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"""
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resource_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask", "attn_mask_startend_row_indices"]
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max_model_input_sizes = MAX_MODEL_INPUT_SIZES
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pretrained_resource_files_map = {
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"vocab_file": {
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"__internal_testing__/tiny-random-qwen2": "https://bj.bcebos.com/paddlenlp/models/community/qwen2/vocab.json",
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},
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}
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def __init__(
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self,
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vocab_file,
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merges_file,
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errors="replace",
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unk_token="<|endoftext|>",
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bos_token=None,
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eos_token="<|endoftext|>",
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pad_token="<|endoftext|>",
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clean_up_tokenization_spaces=False,
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split_special_tokens=False,
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**kwargs,
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):
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if unk_token is None:
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logger.info("The `unk_token` parameter needs to be defined: we use `eos_token` by default.")
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unk_token = eos_token
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# Qwen vocab does not contain control tokens; added tokens need to be special
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bos_token = (
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AddedToken(bos_token, lstrip=False, rstrip=False, special=True, normalized=False)
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if isinstance(bos_token, str)
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else bos_token
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)
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eos_token = (
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AddedToken(eos_token, lstrip=False, rstrip=False, special=True, normalized=False)
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if isinstance(eos_token, str)
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else eos_token
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)
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unk_token = (
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AddedToken(unk_token, lstrip=False, rstrip=False, special=True, normalized=False)
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if isinstance(unk_token, str)
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else unk_token
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)
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pad_token = (
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AddedToken(pad_token, lstrip=False, rstrip=False, special=True, normalized=False)
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if isinstance(pad_token, str)
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else pad_token
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)
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with open(vocab_file, encoding="utf-8") as vocab_handle:
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self.encoder = json.load(vocab_handle)
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self.decoder = {v: k for k, v in self.encoder.items()}
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self.errors = errors # how to handle errors in decoding
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self.byte_encoder = bytes_to_unicode()
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self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
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bpe_merges = []
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with open(merges_file, encoding="utf-8") as merges_handle:
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for i, line in enumerate(merges_handle):
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line = line.strip()
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if (i == 0 and line.startswith("#version:")) or not line:
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continue
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bpe_merges.append(tuple(line.split()))
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self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
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# NOTE: the cache can grow without bound and will get really large for long running processes
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# (esp. for texts of language that do not use space between word, e.g. Chinese); technically
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# not a memory leak but appears as one.
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# GPT2Tokenizer has the same problem, so let's be consistent.
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self.cache = {}
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self.pat = re.compile(PRETOKENIZE_REGEX)
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if kwargs.get("add_prefix_space", False):
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logger.warning_once(
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f"{self.__class__.__name} does not support `add_prefix_space`, setting it to True has no effect."
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)
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super().__init__(
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errors=errors,
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bos_token=bos_token,
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eos_token=eos_token,
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pad_token=pad_token,
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unk_token=unk_token,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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split_special_tokens=split_special_tokens,
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**kwargs,
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)
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@property
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def vocab_size(self) -> int:
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return len(self.encoder)
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def get_vocab(self):
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return dict(self.encoder, **self.added_tokens_encoder)
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def bpe(self, token):
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if token in self.cache:
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return self.cache[token]
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word = tuple(token)
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pairs = get_pairs(word)
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if not pairs:
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return token
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while True:
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
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if bigram not in self.bpe_ranks:
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break
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first, second = bigram
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new_word = []
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i = 0
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while i < len(word):
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try:
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j = word.index(first, i)
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except ValueError:
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new_word.extend(word[i:])
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break
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else:
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new_word.extend(word[i:j])
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i = j
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if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
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new_word.append(first + second)
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i += 2
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else:
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new_word.append(word[i])
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i += 1
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
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word = " ".join(word)
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self.cache[token] = word
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return word
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def _tokenize(self, text):
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"""Tokenize a string."""
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bpe_tokens = []
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for token in re.findall(self.pat, text):
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token = "".join(
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self.byte_encoder[b] for b in token.encode("utf-8")
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) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
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bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
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return bpe_tokens
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.encoder.get(token, self.added_tokens_encoder.get(token, len(self.encoder)))
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.decoder.get(index, self.added_tokens_decoder.get(index, self.unk_token))
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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text = "".join(tokens)
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text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
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return text
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def _decode(
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self,
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token_ids,
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skip_special_tokens: bool = False,
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clean_up_tokenization_spaces: Optional[bool] = False,
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spaces_between_special_tokens: bool = False,
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**kwargs,
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) -> str:
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# `spaces_between_special_tokens` defaults to True for _decode in slow tokenizers
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# and cannot be configured elsewhere, but it should default to False for Qwen2Tokenizer
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return super()._decode(
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token_ids,
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skip_special_tokens=skip_special_tokens,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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spaces_between_special_tokens=spaces_between_special_tokens,
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**kwargs,
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)
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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if not os.path.isdir(save_directory):
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logger.error(f"Vocabulary path ({save_directory}) should be a directory")
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return
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vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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merge_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
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)
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with open(vocab_file, "w", encoding="utf-8") as f:
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f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
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index = 0
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with open(merge_file, "w", encoding="utf-8") as writer:
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writer.write("#version: 0.2\n")
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for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
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if index != token_index:
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logger.warning(
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f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
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" Please check that the tokenizer is not corrupted!"
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)
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index = token_index
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writer.write(" ".join(bpe_tokens) + "\n")
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index += 1
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return vocab_file, merge_file
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def prepare_for_tokenization(self, text, **kwargs):
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text = unicodedata.normalize("NFC", text)
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return (text, kwargs)
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def _encode_chat_inputs(
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self,
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conversations: List[List[str, str]],
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context_data: Dict[str, Any] = {},
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system: str = None,
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add_generation_prompt=True,
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):
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result = {}
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# Some template do not support system msg, so we need to check it first.
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if system:
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try:
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self.chat_template.render(messages={"role": "system", "content": system})
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except Exception as e:
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raise ValueError("System is not supported in this tokenizer.", e)
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# convert list msg to role dict msg
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conversation_dict = []
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origin_msg = []
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for round in conversations:
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round_role = [
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{"role": "user", "content": round[0]},
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{"role": "assistant", "content": round[1]},
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]
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origin_msg.extend(round_role)
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conversation_dict.append(round_role)
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# Get system string in ChatTemplate
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# ChatTemplate contains three parts: system, user, and assistant.
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# However, the system string cannot be obtained directly with the chat_template.render() function.
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# Thus, three steps are needed to extract the system string.
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# Step 1: Obtain the combined system and user string in the first round.
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# Step 2: Obtain the special system string.
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# Step 3: Obtain the special combined system and user string in the first round.
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# Then, user string = (special system and user string) - (special system string)
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# And, system string = (initial system and user string) - (user string)
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assert len(conversation_dict) > 0, "conversations is empty"
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def replace_first_occurrence(original_string, to_find, to_replace):
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index = original_string.find(to_find)
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if index == -1: # to_find not found in original_string
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return original_string
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else:
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return original_string[:index] + to_replace + original_string[index + len(to_find) :]
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if system:
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system_str = self.chat_template.render([system])
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else:
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# get system and user str
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round0_str = self.chat_template.render(
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messages=conversation_dict[0][:1], add_generation_prompt=False, **self.special_tokens_map
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)
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# get special system str
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round0_only_system_str = self.chat_template.render(
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messages=[{"role": "system", "content": ""}], add_generation_prompt=False, **self.special_tokens_map
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)
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# get special system and user str
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round0_system_user_str = self.chat_template.render(
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messages=[{"role": "system", "content": ""}] + conversation_dict[0][:1],
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add_generation_prompt=False,
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**self.special_tokens_map,
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)
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# get user str = {special system and user str} - {special system str}
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user_str = replace_first_occurrence(round0_system_user_str, round0_only_system_str, "")
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# get system str = { system and user str} - {user str}
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system_str = round0_str.replace(user_str, "")
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no_ans = []
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ans = []
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for conv in conversation_dict:
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roundi = [system] + conv if system else conv
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roundi_str = self.chat_template.render(
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messages=roundi, add_generation_prompt=False, **self.special_tokens_map
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)
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roundi_no_ans = [system] + [conv[0]] if system else [conv[0]]
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roundi_no_ans_str = self.chat_template.render(
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messages=roundi_no_ans, add_generation_prompt=add_generation_prompt, **self.special_tokens_map
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)
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roundi_ans_str = roundi_str[len(roundi_no_ans_str) :]
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ans.append(roundi_ans_str)
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roundi_no_ans_no_system_str = replace_first_occurrence(roundi_no_ans_str, system_str, "")
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assert (
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roundi_no_ans_str == system_str + roundi_no_ans_no_system_str
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), f"the src string contains system str: {system_str}"
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no_ans.append(roundi_no_ans_no_system_str)
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# the first round is special, we need to add system_str
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no_ans[0] = system_str + no_ans[0]
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conversation_ids = []
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for i in range(len(no_ans)):
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conversation_ids.append(
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self.batch_encode(
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[no_ans[i], ans[i]],
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add_special_tokens=False,
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padding=False,
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)["input_ids"]
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)
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result["conversations"] = conversation_ids
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return result
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